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Polymicrogyric Cortex may Predispose to Seizures via Abnormal Network Topology: An fMRI Connectomics Study
Author(s) -
Sethi Moksh,
Pedersen Mangor,
Jackson Graeme D.
Publication year - 2016
Publication title -
epilepsia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.687
H-Index - 191
eISSN - 1528-1167
pISSN - 0013-9580
DOI - 10.1111/epi.13304
Subject(s) - polymicrogyria , connectomics , connectome , clustering coefficient , neuroscience , epilepsy , functional magnetic resonance imaging , cortex (anatomy) , computer science , psychology , cluster analysis , functional connectivity , artificial intelligence
Summary Polymicrogyria is a significant malformation of cortical development with a high incidence of epilepsy and cognitive deficits. Graph theoretic analysis is a useful approach to studying network organization in brain disorders. In this study, we used task‐free functional magnetic resonance imaging ( fMRI ) data from four patients with polymicrogyria and refractory epilepsy. Gray matter masks from structural MRI data were parcellated into 1,024 network nodes. Functional “connectomes” were obtained based on fMRI time series between the parcellated network nodes; network analysis was conducted using clustering coefficient, path length, node degree, and participation coefficient. These graph metrics were compared between nodes within polymicrogyric cortex and normal brain tissue in contralateral homologous cortical regions. Polymicrogyric nodes showed significantly increased clustering coefficient and characteristic path length. This is the first study using functional connectivity analysis in polymicrogyria—our results indicate a shift toward a regular network topology in polymicrogyric nodes. Regularized network topology has been demonstrated previously in patients with focal epilepsy and during focal seizures. Thus, we postulate that these network alterations predispose to seizures and may be relevant to cognitive deficits in patients with polymicrogyria.